def init_opt_ipopt_get_statistics(self):
"""
Get statistics from the last optimization run.
Returns::
return_status --
The return status from IPOPT.
nbr_iter --
The number of iterations.
objective --
The final value of the objective function.
total_exec_time --
The execution time.
"""
return_code = ct.c_int()
iters = ct.c_int()
objective = c_jmi_real_t()
exec_time = c_jmi_real_t()
if self._nlp_init._jmi_model._dll.jmi_init_opt_ipopt_get_statistics(
self._ipopt_init, byref(return_code), byref(iters),
byref(objective), byref(exec_time)) is not 0:
raise JMIException(
"Error when retrieve statistics - optimization problem may not be solved."
)
return (return_code.value, iters.value, objective.value,
exec_time.value)
def __init__(self, nlp_init):
"""
Class for solving a DAE initialization problem my means of optimization
using IPOPT.
Parameters::
nlp_init --
The NLPInitialization object.
"""
self._nlp_init = nlp_init
self._ipopt_init = ct.c_voidp()
self._set_initOpt_typedefs()
try:
assert self._nlp_init._jmi_model._dll.jmi_init_opt_ipopt_new(
byref(self._ipopt_init), self._nlp_init._jmi_init_opt) == 0, \
"jmi_init_opt_ipopt_new returned non-zero"
except AttributeError as e:
raise JMIException(
"Can not create InitializationOptimizer object. \ "
"Please recompile model with target='ipopt")
assert self._ipopt_init.value is not None, \
"jmi struct not returned correctly"
def init_opt_set_initial_from_model(self):
"""
Set the initial point of the NLP based on values in the JMI model.
"""
if self._jmi_model._dll.jmi_init_opt_set_initial_from_model(
self._jmi_init_opt) is not 0:
raise JMIException("Could not set initial point from model.")
def init_opt_f(self, f):
"""
Get the cost function value at a given point in search space.
Parameters::
f --
The value of the cost function. (Return variable)
"""
if self._jmi_model._dll.jmi_init_opt_f(self._jmi_init_opt, f) is not 0:
raise JMIException("Getting the cost function failed.")
def init_opt_set_initial(self, x_init):
"""
Set the initial point of the NLP.
Parameters::
x_init --
The initial guess vector.
"""
if self._jmi_model._dll.jmi_init_opt_set_initial(
self._jmi_init_opt, x_init) is not 0:
raise JMIException("Setting the initial point failed.")
def init_opt_h(self, res):
"""
Get the residual of the equality constraints h.
Parameters::
res --
The residual of the equality constraints. (Return variable)
"""
if self._jmi_model._dll.jmi_init_opt_h(self._jmi_init_opt,
res) is not 0:
raise JMIException(
"Getting the residual of the equality constraints failed.")
def init_opt_ipopt_set_num_option(self, key, val):
"""
Set an Ipopt double option.
Parameters::
key --
The name of the option.
val --
The value of the option.
"""
if self._nlp_init._jmi_model._dll.jmi_init_opt_ipopt_set_num_option(
self._ipopt_init, key, val) is not 0:
raise JMIException("The Ipopt real option " + key + " is unknown")
def init_opt_dh(self, jac):
"""
Get the Jacobian of the residual of the equality constraints.
Parameters::
jac --
The Jacobian of the residual of the equality constraints.
(Return variable)
"""
if self._jmi_model._dll.jmi_init_opt_dh(self._jmi_init_opt,
jac) is not 0:
raise JMIException(
"Getting the Jacobian of the residual of the equality constraints."
)
def init_opt_set_bounds(self, x_lb, x_ub):
"""
Set the upper and lower bounds of the optimization variables.
Parameters::
x_lb --
The lower bounds vector. (Return variable)
x_ub --
The upper bounds vector. (Return variable)
"""
if self._jmi_model._dll.jmi_init_opt_set_bounds(
self._jmi_init_opt, x_lb, x_ub) is not 0:
raise JMIException(
"Getting upper and lower bounds of the optimization variables failed."
)
def init_opt_ipopt_solve(self):
"""
Solve the NLP problem.
"""
if self._nlp_init._jmi_model._dll.jmi_init_opt_ipopt_solve(
self._ipopt_init) > 1:
raise JMIException("Solving IPOPT failed.")
# Check return status from Ipopt and raise exception if not ok
(return_status, nbr_iters, obj_final,
tot_exec_time) = self.init_opt_ipopt_get_statistics()
# Return code should be one of (taken from IpReturnCodes.inc):
# 0: IP_SOLVE_SUCCEEDED
# 1: IP_ACCEPTABLE_LEVEL
# 6: IP_FEASIBLE_POINT_FOUND
if return_status not in (0, 1, 6):
raise IpoptException("Ipopt failed with return code: " + str(return_status) + \
" Please see Ipopt documentation for more information.")
def init_opt_dh_nz_indices(self, irow, icol):
"""
Get the indices of the non-zeros in the equality constraint Jacobian.
Parameters::
irow --
The row indices of the non-zero entries in the Jacobian of the
residual of the equality constraints. (Return variable)
icol --
The column indices of the non-zero entries in the Jacobian of
the residual of the equality constraints. (Return variable)
"""
if self._jmi_model._dll.jmi_init_opt_dh_nz_indices(
self._jmi_init_opt, irow, icol) is not 0:
raise JMIException(
"Getting the indices of the non-zeros in the equality constraint Jacobian failed."
)
def init_opt_get_dimensions(self):
"""
Get the number of variables and the number of constraints in the
problem.
Returns::
Tuple with the number of variables in the NLP problem, equality
constraints, and non-zeros in the Jacobian of the equality
constraints respectively.
"""
n_real_x = ct.c_int()
n_h = ct.c_int()
dh_n_nz = ct.c_int()
if self._jmi_model._dll.jmi_init_opt_get_dimensions(
self._jmi_init_opt, byref(n_real_x), byref(n_h),
byref(dh_n_nz)) is not 0:
raise JMIException(
"Getting the number of variables and constraints failed.")
return n_real_x.value, n_h.value, dh_n_nz.value
